The amount of data generated by modern test equipment and measurement equipment has increased to the point that test personnel cannot understand the data without the help of data processing systems to summarize various aspects of the large data sets generated by such equipment. Typically, each test generates a record which includes a value for the parameter or parameters being measured together with a number of parameters that describe the conditions under which the measurement was made. For example, a test of a communication circuit board might measure the bit error rate or jitter of the communication signal. In addition, various environmental parameters such as the temperature of a component on the board, the ambient temperature at the test apparatus, the operator of the test equipment, and the identity of one or more component parts of the circuit board may also be noted. The additional parameters will be referred to as the measurement conditions in the following discussion, while the measured bit error rate and the jitter will be referred to as the measured values. On a production line for qualifying the circuit boards, hundreds of thousands of these records can be generated. Understanding a data set of this magnitude requires a level of data analysis that is greater than a test engineer can provide without some form of computer system.
In such testing systems, determining the measurement conditions that may explain an anomaly in the measured values presents significant challenges for an engineer who is not also a skilled programmer.